An Unsupervised, Iterative N-Dimensional Point-Set Registration Algorithm
This addresses a domain-specific challenge in computer vision or robotics for aligning point clouds without labels, but appears incremental as it builds on existing linear least squares methods.
The paper tackled the problem of unsupervised point-set registration in N-dimensional Euclidean space without known correspondences, proposing an iterative algorithm based on linear least squares that aligns point sets until one-to-one pairings are maximized, but no concrete numerical results or performance metrics are provided.
An unsupervised, iterative point-set registration algorithm for an unlabeled (i.e. correspondence between points is unknown) N-dimensional Euclidean point-cloud is proposed. It is based on linear least squares, and considers all possible point pairings and iteratively aligns the two sets until the number of point pairs does not exceed the maximum number of allowable one-to-one pairings.